SCHOOL OF ECONOMICS AND FINANCE

Discussion Paper 2013-04

Chinese Monetary Expansion and the US Economy

Joaquin L Vespignani and Ronald A Ratti

ISSN 1443-8593 ISBN 978-1-86295-714-5

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Chinese monetary expansion and the U.S. economy

Joaquin L. Vespignania*

and Ronald A. Rattib**

aUniversity of Tasmania, School of Economics and Finance, Australia

bUniversity of Western Sydney, School of Business, Australia

Abstract

This paper examines the influence of monetary shocks in China on the U.S. economy over

1996-2012. The influence on the U.S. is through the sheer scale of China’s growth through

effects in demand for imports, particularly that of commodities. China’s growth influences

world commodity/oil prices and this is reflected in significantly higher inflation in the U.S.

China’s monetary expansion is also associated with significant decreases in the trade

weighted value of the U.S. dollar that is due to the operation of a pegged currency. China

manages the exchange rate and has extensive capital controls in place. In terms of the

Mundell–Fleming model, with imperfect capital mobility, sterilization actions under a

managed exchange rate permit China to pursue an independent monetary policy with

consequences for the U.S.

Keywords: International monetary transmission, China’s monetary aggregates

JEL Codes: E52, F41, F42

*Corresponding author: Joaquin L. Vespignani; University of Tasmania, School of Economics and Finance,

Australia; Tel. No: +61 3 62262825; E-mail address: Joaquin.Vespignani@utas.edu.au

**

Ronald A. Ratti; University of Western Sydney, School of Business, Australia; Tel. No: +61 2 9685 9346; E-

mail address: r.ratti@uws.edu.au

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Chinese monetary expansion and the U.S. economy

1. Introduction

This paper examines the influence of monetary policy shocks in China on the U.S.

economy over 1996-2012. The topic is of interest given the growing importance of China in

the world economy. In 2009 China’s M2 measured in U.S. dollars surpassed that in the U.S.

for the first time and by the end of 2012 exceeded that in the U.S. by 50%.1 The growing

importance of China’s money supply is illustrated in Figure 1. Given these developments,

does a monetary expansion in China affect the U.S. economy?

Intertemporal models by Svensson and Van Wijnbergen (1989) and Obstfeld and

Rogoff (1995) suggest that monetary expansion in a large open economy such as the U.S.

will decrease world real interest rates and stimulate global aggregate demand in the U.S. and

non-U.S. countries. Support for an interest rate mechanism of monetary expansion in the U.S.

causing economic expansion in the non-U.S. G-6, Canada, and Latin America is confirmed

by Kim (2001a), Holman and Neumann (2002), and Canova (2005), respectively.

China operates a dollar peg and has extensive capital controls in place. How then

would China’s monetary policy influence the U.S. economy? In Keynesian models with

prices inflexible in labour or goods markets, as in the Mundell–Fleming model, a fixed

exchange rate ties domestic to foreign interest rates when there is arbitrage in asset markets.

A monetary expansion starts a temporary fall in the domestic interest rate relative to foreign

interest rates, with the resulting loss of foreign exchange reserves leading to a reversal in the

fall of the domestic interest rate. The impossible trinity holds, in that it is not possible to

simultaneously have a fixed exchange rate, perfect capital movement and autonomous

monetary policy. In the Mundell–Fleming model with imperfect capital mobility, sterilization

1 China’s nominal M2 (in USD) increased on average by 19.5% per year from 1996 to 2011. The behaviour of

China’s nominal GDP is also strongly upward. From 1996 to 2011 China’s nominal GDP (in USD) increased on

average by 15% per year. China’s real M2 also has a strong upward trend. Based on an index of 100 in 1996, by

2011:12 China’s real M2 index (the deflator is the U.S. PPI index for commodities) is 340.

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actions under a fixed exchange rate permit an independent monetary policy for as long as

foreign exchange reserves permit.

Goodfriend and Prasad (2007) argue that capital controls provide room for monetary

policy independence in China even though the central bank manages the exchange rate. Sun

(2009) argues that China operated an independent monetary policy during the fixed exchange

rate period 1998 to 2005. Up until 2005 China’s renminbi was pegged to the U.S. dollar.

Since 2005 the renminbi has been allowed to float in a narrow margin around a fixed base

rate determined with reference to a basket of major currencies with undisclosed weights. This

is illustrated in Figure 2, with a tendency for a fall in the China/U.S. exchange rate since 2005

as the U.S. dollar weakened against other currencies.

The influence on the U.S. is through the sheer scale of China’s growth through effects

in demand for imports, particularly that of commodities.2 China’s growth influences world

commodity prices and this is reflected in significantly higher inflation in the U.S. China’s

monetary expansion is also associated with significant decreases in the trade weighted value

of the U.S. dollar. With domestic expansion and an increase in imports, China intervenes in

the foreign exchange market to stabilize the exchange rate and sells foreign currency. Pre

2005 with the Renminbi pegged to the U.S. dollar, the effect of a monetary expansion in

China is stabilized by the net sale of U.S. dollars. Recent empirical studies by Cai et al.

(2012) and Fang et al. (2012) show that post 2005, consideration of the parity of the exchange

rate of the renminbi against the U.S. dollar dominates the influence of the other currencies in

determining the overall value of the renminbi with reference to a basket of major currencies.

2 China has a 6% annual growth rate in petroleum consumption since 1998. Hamilton (2011) notes that the

newly industrialized economies, among which China has a leading role, have absorbed over two-thirds of the

increase in world oil consumption since 1998. Radetzki (2006) notes that between 2000 and 2005 China’s share

of global demand growth for petroleum was 28%, for aluminium was more than 50%, for steel was more than

84%, and for copper was 95%. Radetzki (2006) surmises that in developing Asian countries a dollar added to

the GDP uses more than twice the quantity of commodities as does a dollar added to the GDP in OECD

countries. China’s oil intake is forecast to be 17.5 million barrels per day by 2030, overtaking the United States

as the world’s largest oil consumer (World Energy Outlook 2012). World Energy Outlook to be found at:

http://iea.org/publications/freepublications/publication/English.pdf.

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We find that monetary expansion in China is associated with a statistically significant decline

in the real trade-weighted U.S. dollar exchange rate.

The following results are found to be robust across a number of model specifications.

A positive innovation in China’s M2 is associated with a positive, significant and persistent

effect on oil price (and on commodity prices); a positive, significant and persistent effect on

U.S. CPI inflation; a significant and persistent depreciation of the real trade-weighted U.S.

currency; a significant and persistent depreciation of the U.S. terms of trade; positive,

significant and persistent increases in price deflators for U.S. exports and imports; positive,

significant and persistent increases in U.S. exports, imports; reduction in real U.S. trade

deficit. Shocks to China’s M2 have stronger impact on U.S. variables since about 2005. This

is consistent with the growth of China’s economy. The cumulative contribution of China M2

shocks on the U.S. consumer price index and on oil price are much larger in the last half of

sample. The increase in import prices (attendant on a decline in the trade-weighted U.S.

dollar exchange rate) together with the increase in oil prices account for the association of

innovations to China’s M2 with increases in U.S. consumer price inflation.

In this investigation we use a number of structural vector autoregressive models to

examine the effects of monetary policy shocks in China on U.S. output, inflation, monetary

aggregates, interest rate, exchange rate, terms of trade and trade variables. The model is

discussed in section 2, empirical results are presented in section 3 and section 4 concludes.

2. The Methodology

Using monthly macroeconomic variables for the U.S. economy, we introduce the

effect of Chinese monetary aggregates by constructing a structural vector autoregressive

model (SVAR). Monthly variables selection is based on Sims and Zha (1995) and Kim and

Roubini (2000) to the extent possible given that the impact of China’s monetary expansion on

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the U.S. is being considered. The variables are: China’s M2 in U.S. dollars, the

short term U.S. interest rate , U.S. M2 , the U.S. consumer price index

, the U.S. industrial production , oil prices in U.S. dollars (or global

commodity price index in U.S. dollars), the real effective trade-weighted U.S. dollar

exchange rate ).

Fan et al. (2011) observe that the central bank of China maintains that the money

supply is the main monetary tool in China. Fan et al. (2011) confirm that the growth rate in

money supply (M2) plays a crucial role in fine-tuning China’s economy, while official

interest rates played a very passive role. Koz´luk and Mehrotra (2009) and Johansson (2012)

use M2 as the measure of China’s monetary policy in studies on the influence of China on

East and Southeast Asian economies.

The exchange rate policy of China is of crucial importance in assessing the impact of

China’s monetary expansion on the U.S. economy. A rise in China’s M2 facilitates domestic

growth and increases demand for imports. The currencies of the countries supplying imports

to China experience upward pressure. To stabilize the pegged exchange rate, China must

intervene in the foreign exchange market and sell foreign currency.3 Prior to 2005, with the

renminbi pegged to the U.S. dollar, the consequence of an increase in China’s M2 would be a

devaluation of the U.S. dollar relative to other countries. Since July 21, 2005 the value of

renminbi has been determined with regard to a basket of currencies among which the dollar

has been pre-eminent. Indeed, over an extended period from 2008 to 2010 the renminbi/dollar

rate didn’t vary from 6.8 (as illustrated in Figure 2). It is hypothesized that positive

innovations in China’s M2 will depress the real trade-weighted U.S. dollar exchange rate.

3 The net effect of these actions on the real trade-weighted U.S. dollar exchange rate depends on the countries of

origin of imports to China, the foreign currencies sold by China in the foreign exchange market, and the weights

assigned to currencies in the reference basket of major currencies (dominated by the U.S. dollar, Euro, Japanese

yen and South Korean won) to which the renminbi is allowed to float within a narrow margin.

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The SVAR model is expressed in matrix form as (for simplicity the constant term is

omitted):

∑ ∑

∑

(1)

Where:

[

] (2)

where j is the optimal lag length, determined by the Akaike information criterion (AIC) (two

lags in this case), tX is vector of endogenous variables, are the individual vectors of

country-individual industrial production of China , Euro area and Japan ) and are

vectors of the country-individual M2 for Euro area and Japan ). Those exogenous vectors

are introduced to the model to tackle the possible problem of omitted variables bias as U.S.

industrial production may be correlated with largest economies such as; China, Euro area and

Japan industrial production and both China and U.S. M2 may be correlated with Euro area

and Japan’s M2. Equation (2) is shown with oil price representing commodity prices. Results

are not affected if a global commodity price index replaces oil price in the SVAR.4 Finally,

t is an error term vector, which is serially and mutually independent.

2.1. Identification strategy

In line with Bernanke (1986), Sims and Zha (1995), Kim and Roubini (2000) and Kim

(2001a; 2001b) non-recursive identify restrictions are proposed in the contemporaneous

structure. The contemporaneous matrix can be express as:

4 In SVAR models Christiano et al.’s (1999) (for the U.S.) and Dedola and Lippi (2005) (for five OECD

countries) utilize a global commodity price index as a variable and Kim and Roubini (2000) (for industrial

economies) utilize oil price.

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[

]

(

)

(3)

The restrictions in equation (3) are essentially those in Kim and Roubini (2000) but

with China’s M2 entered as contemporaneously exogenous to the other variables. Kim and

Roubini (2000) assume real output and oil prices (in the fifth and sixth equations

respectively) are contemporaneously exogenous to all variables in the model due to

information delay. The delay in the transition of the Chinese M2 and oil prices are

substantiated by the fact that these variables are exogenous to the U.S. economy. However,

previous studies have restricted real output in two different ways; Kim and Roubini (2000)

and Kim (2001b) allows real output to depend contemporaneously on oil prices while

Bagliano and Favero (1998) and Anzuini et al. (2012) treats real output as

contemporaneously exogenous of all variables.5 Using a log likelihood ratio test for over-

identification restrictions, results for our data support restrictions in favour of real output to

be contemporaneously exogenous.6

Consistent with Sims and Zha (1995)’s dynamic stochastic general equilibrium

model, the monetary policy feedback rule is based on the recognition of information delays

that do not allow the monetary policy to respond within the month to price level and output

events. The monetary policy rule only responds contemporaneously to U.S. M2, oil prices

and the real effective trade-weighted U.S. exchange rate. Following the literature, the M2

monetary aggregate responds contemporaneously to the domestic interest rate, inflation and

industrial production implying that real demand for money depends on the interest rate and

real income.

5 Note that Bagliano and Favero (1998) use commodity prices instead of oil prices.

6 The Chi-square value for treating real output as contemporaneously exogenous is 10.5, while the Chi-square

value for assuming real output depends contemporaneously on oil prices is 9.98.

8

In line with Kim and Roubini (2000) and Kim (2001a; 2001b) inflation is affected

contemporaneously by real output and oil prices consistent with the notion of

contemporaneous demand pull and cost push inflation forces. The real effective trade-

weighted U.S. exchange rate responds contemporaneously to all variables but Chinese M2

given its forward-looking properties and the fact that exchange rates operate daily looking at

current data and economic activity.7 Alternative identification strategies are explored in

examination of the robustness of results.

2.2. The Data and unit root test

The data are monthly from 1996:1 to 2012:12. The starting date is dictated by the first

monthly observation of Chinese M2 reported by People’s Bank of China. The People’s Bank

of China stopped bank credit targeting in 1998 and started concentrating on balance sheet

adjustment for the conduct of monetary policy (Johansson (2012). Monetary aggregates,

industrial production indexes, U.S. CPI, interest rate, oil prices (West Texas Intermediate

crude oil) and commodity price data are from the Federal Reserve of St. Louis (FRED), while

Chinese M2 is from People’s Bank of China. The U.S. trade prices and variables are from the

U.S. Bureau of Labor Statistics.

To avoid spurious regression problem, we test the data for unit root process. Table 1

reports test results for unit roots in the variables over 1996:1-2012:12. The null hypothesis for

the Augmented Dickey-Fuller (ADF) test is that the variable has a unit root and the null

hypothesis for the Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test is that the variable is

stationary. The first difference of the series is indicated by ∆. The lag selection criteria for the

ADF test is based on Schwarz information Criteria (SIC) and for the KPSS is the Newey-

West Bandwidth with constant and linear trend. In table 1, the Augmented Dickey Fuller test

(ADF) unit root tests reveals that the logs of all endogenous variables in the model are only

7 See for example Bagliano and Favero (1998), Kim and Roubini (2000) and Kim (2001a; 2001b).

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first difference stationary. These results are confirmed by Kwiatkowski-Phillips-Schmidt-

Shin (KPSS), where the inverse null hypothesis is tested.8 Consequently, and following the

literature using mostly data the model is estimated in first difference.

3. Empirical Results

3.1. The impulse response results

Figure 3 shows the dynamic response or impulse response function of the U.S.

variables in the SVAR in equation (3) to a one per cent change in China M2 shocks. The

dashed lines represent a one standard error confidence band around the estimates of the

coefficients of the impulse response functions.9

In the first row in Figure 3, the short term U.S. interest rate does not respond

significantly to innovations in China’s M2. An unanticipated positive increase in China’s M2

has a negative effect on U.S. M2 that is statistically significant. This latter result is consistent

with a defensive response by the U.S. to a stimulus by China, which boosts U.S. CPI inflation

and industrial production and weakens the U.S. dollar as discuss later. A positive innovation

in China’s M2 has statistically significant positive effect on the U.S. CPI. The effect builds

up over five months and then persists. In the second row in Figure 3, U.S. industrial

production is positively affected by positive innovations in China’s M2, but the effect is not

statistically significant.

A positive shock in China’s M2 has a positive and statistically significant effect on oil

price that builds up rapidly over three months and then persists for twenty months in the

second row in Figure 3. The effect of China’s growth on real oil price has been documented

in the literature. Hamilton (2011) notes that rapid growth in oil consumption in China and in

8 Results are also supported by the Dickey Fuller GLS (DF-GLS) and the Phillip-Perron (PP) which are

available upon request. In addition, all exogenous variables in the model are also first different stationary (data

available upon request). 9 The confidence bands are obtained using Monte Carlo integration as described by Sims (1980), where 5000

draws were used from the asymptotic distribution of the VAR coefficient.

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other emerging countries is the most important factor influencing oil prices in recent years.

Kilian and Hicks (2012) associate the rise in real oil price over 2003-2008 with unexpected

growth in emerging economies (primarily China and India). Ratti and Vespignani (2013) find

a large cumulative impact of China’s real M2 on the real price of crude oil over the last

fifteen years. In results not shown, if a global commodity price variable replaces the oil price

variable in the SVAR in equation (2) results are similar. A positive innovation in China’s M2

has a positive and statistically significant effect on commodity price that rises sharply over

three months, peaks at about five months, and then persists for twenty months.

The response effects of the real trade-weighted U.S. dollar to shocks to China’s M2, is

shown in the last diagram in Figure 4. A rise in China’s M2 is associated with a statistically

significant negative effect on the real trade-weighted U.S. currency after two months that

persists for twenty months. The effect of an increase in China’s M2 might be expected to be

less marked on the real trade-weighted U.S. dollar exchange rate since 2005 (with the

renminbi tied to a band around a basket of world currencies rather than being pegged to the

dollar). However, it should be noted that the scale of China’s influence on the global

economy in the last half of the sample is much greater than in the first half of the sample and

that considerable weight has been given to the U.S. dollar in the basket of major currencies

around which central parity the value of renminbi is allowed to vary since 2005. In results not

reported, it is found that when the sample is broken in July 2005, a positive innovation in

China’s M2 is associated with a statistically significant negative effect on the real trade-

weighted U.S. foreign exchange rate post July 2005.

3.2. Responses of U.S. trade variables to China M2 shocks

Given the finding that a positive shock to China’s M2 leads to a statistically

significant and persistent depreciation of the real trade-weighted value of U.S. currency, the

impact of shocks to China’s M2 on U.S. trade and trade price variables will be examined.

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Following a procedure in Kim (2001a) the price deflator for U.S. exports, price

deflator for U.S. imports, and U.S. terms of trade variable (U.S. export price/U.S. import

price) are now added one variable at a time as an additional variable in the SVAR system

(equation 2 and 3). The impulse response functions of the U.S. trade price variables to a one

per cent change in China M2 shocks are shown in Figure 4. An unanticipated rise in China’s

M2 significantly raises the price deflator for U.S. exports and the price deflator for U.S.

imports. The rise in both price deflators is completed after five months and then persists over

the twenty months shown. The rise in the price deflator for U.S. exports is consistent with the

finding that a positive innovation in China’s M2 has a positive and statistically significant

effect on U.S. CPI inflation that builds up over several months and then persists. The rise in

the U.S. price deflator for U.S. imports in response to a positive innovation in China’s M2

ties in with the statistically significant depreciation of the real trade-weighted value of U.S.

exchange rate. A positive shock in China’s M2 results in a rise in the U.S. dollar price of

imports for U.S. consumers.

The impulse response of the U.S. terms of trade to a positive shock in China’s M2 is

shown in the third diagram in Figure 4. The U.S. terms of trade decline following a positive

shock to China’s M2. The decline in the terms of trade is statistically significant after three

months, achieves maximum effect at four months, and then persists almost unchanged.

Although a positive shock in China’s M2 significantly raises export prices, the effect on

import prices is so much greater that the terms of trade significantly decline. This is an

important result since changes to the terms of trade affect consumption decisions, the

composition of output across industries and welfare, and channel international shocks.

In figure 5 the impulse response function of Chinese M2 shocks on the U.S. trade

variables: nominal exports, imports and trade balance and real exports, imports and trade

balance are reported. These results are obtained from SVAR models in which these variables

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are added one variable at a time as an additional variable in the SVAR model in equations (2)

and (3).

In Figure 5 a positive shock to China’s M2 significantly expands both U.S. nominal

and real exports. Foreign demand for U.S. goods is stimulated by the fall in the U.S. dollar.

The effect of a shock to China’s M2 on real U.S. exports is statistically significant after three

months, with the rise in real exports completed after six months after which the effect

persists. A positive innovation in China’s M2 has a negative effect on real U.S. imports that

(with the exception of a significant effect at three months) is not statistically significant. U.S.

demand for foreign goods is relatively price inelastic with the result that the nominal dollar

value of U.S. imports is significantly increased. The net consequences of these effects on

exports and imports are that a positive shock to China’s M2 does not significantly impact the

U.S. nominal trade balance, but does significantly improve the U.S. trade balance in real

terms.10

In Figure 5 a positive shock to China’s M2 has a significant effect on the U.S. trade

balance in real terms from three months onwards. The effect is highly persistent.

An interesting issue that arises is whether the effects of shocks to China’s M2 on U.S.

trade variables are driven by bilateral China/U.S. trade effects or non-China/U.S. trade effects

or both. In Figure 6 it is shown that except in the second month, a positive shock to China’s

M2 does not significantly impact U.S. nominal exports to China or U.S. nominal imports

from China. The significant rise in U.S. nominal exports in response to a positive shock to

China’s M2 is driven by a significant rise in U.S. exports to countries other than China. The

impulse response results for the effect of a positive shock to China’s M2 on U.S. nominal

exports excluding exports to China are shown in Table 6. U.S. dollar exports to the rest of the

world (exclusive of China) rise significantly after three months and the effect persists. In

response to a positive shock to China’s M2, U.S. dollar imports from the rest of the world

10

As in Kim (2001b) real exports (export volume) minus real imports (import volume) is used as a proxy for the

U.S. trade balance in real terms.

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(exclusive of China) also rise significantly after three months and the effect persists.

However, the rise in U.S. dollar exports is significantly greater than the rise in U.S. dollar

imports with respect to the world exclusive of China as shown in Figure 6.

3.3. Historical decomposition of the effects of China’s M2

The cumulative contribution of the structural shocks to China’s M2 to the U.S.

consumer price index, oil prices, the real effective trade-weighted U.S. dollar exchange rate,

the U.S. terms of trade, real trade balance variables and short term U.S. interest rate are

shown in Figure 7. These results are obtained from estimating the SVAR model in equation

(3), with the exception of those for the real trade balance and the terms of trade. Results for

the latter variables are obtained from modifying the model in equation (3) by adding the real

trade balance and the terms of trade variables once at the time to this system. The cumulative

contributions of structural shocks to the variables reported in Figure 7 are annual averages of

the monthly data (the moving average of the last 12 months) to improve the readability of the

plot. The data are not annualized.

Striking facts from Figure 7 are that the cumulative contribution of China’s M2 to the

U.S. variables shown appears to differ in the second half of the sample from the first half of

the sample. Consistent with the growth of China’s economy, shocks to China’s M2 have

stronger impact of China on U.S. variables since about 2005. The cumulative contribution of

China M2 shocks on the consumer price index and on oil price are much larger (more

positive) in the last half of sample. The rapid increase in oil price leading to a peak in June

2008 is associated with a positive contribution from China’s M2. The cumulative impact of

China’s M2 on the price of oil is also positive in the strong recovery of oil price since 2009.

China’s monetary expansion has a positive cumulative contribution to the U.S. consumer

price index since 2005 in contrast to a largely negative cumulative contribution to the U.S.

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consumer price index before 2005. In 2005 the annualized contribution of China’s M2 to US

CPI is up to 0.3%.

The cumulative effect of China M2 shocks on the real effective trade-weighted U.S.

dollar exchange rate is largely negative since 2005. In the last half of the sample China’s M2

shocks have tended to contribute to a decline in the trade-weighted U.S. dollar. The effect of

China M2 shocks on worsening the terms of trade for the U.S. in the latter half of the sample

is also quite marked in Figure 7. In Figure 7 the cumulative effect of China M2 shocks on the

U.S. trade balance in real terms is largely negative over 2005 to 2011, i.e. China M2 shocks

tended to result in an improved U.S. trade balance in real terms over these years. This

outcome is consistent with the movements in the terms of trade. The increase in import prices

(attendant on a decline in the trade-weighted U.S. dollar exchange rate) together with the

increase in oil prices account for the association of innovations to China’s M2 with increases

in U.S. consumer price inflation.

The contribution of China’s M2 to U.S. short-term interest rate in Figure 7 show large

positive contributions that overlap with the U.S. recession periods March 2001 to November

2001 and December 2007 to June 2009. During these times global demand was also weak,

but China’s economy and M2 continues to expand. For this reason we observe the pattern of

positive contributions by innovations to China’s M2 to U.S. short-term interest rate in the two

periods indicated.11

3.5. Robustness and alternative identification strategies

We now investigate the robustness of the results using alternative specifications to the

SVAR reported in equation (3). We investigate four alternative non-recursive identification

11

In 2001 period there is the collapse of the speculative dot-com bubble and fall in business outlays and

investments, and in the 2007-2009 period there is the subprime mortgage crisis and collapse of the U.S. real

estate bubble. Global demand was weak during these periods in that both U.S. real export volume and U.S. real

import volume decline sharply during 2001-2002 and 2008-2009.

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restrictions in the contemporaneous matrix restriction to that shown for in equation (3).

The alternative identification schemes based on equations (1) and (2) are the following:

[

]

(

)

(4)

[

]

(

)

(5)

[

]

(

)

(6)

[

]

(

)

(7)

In system (4), the U.S. variables respond contemporaneously to China’s M2. In

system (5), China’s M2 responds to U.S. domestic variables in the same way that U.S. M2

does. In system (6), China’s M2 responds contemporaneously to all U.S. domestic variables.

Finally, in system (7) the real trade-weighted U.S. dollar responds contemporaneously to

China’s M2.

The estimation of the SVAR models outlined in equations (4), (5), (6) and (7) results

in impulse response of the U.S. economic variables to positive shocks to China’s M2 that are

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very similar to those shown in Figure 3. In all models a positive innovation in China’s M2 is

associated with:

1. a positive and statistically significant effect on oil price (or on commodity prices) that

builds up rapidly over three months and then persists for twenty months;

2. a positive and statistically significant effect on U.S. CPI inflation that builds up over

about six months or so and then persists;

3. a statistically significant depreciation of the real trade-weighted U.S. currency after

about two or three months that achieves maximum absolute value after five to eight

months and that then persists.

In Table 2, the log likelihood ratio for over-identification test Chi-square values are

reported for each of the five models shown in equations (3) – (7). The highest value for over-

identification test restriction is for our model of choice in equation (3), indicating that the

restriction cannot be rejected at a higher significance level than for the other models.

4. Conclusion and discussion

The major finding of the paper is that China’s monetary expansion has a spill over

effect on the U.S. through the effects on world commodity markets and through the exchange

rate regime followed by China. Increases in monetary aggregates in China significantly

increase the world prices of oil and commodities, significantly increases U.S. CPI inflation,

and significantly decreases the real trade-weighted value of the U.S. dollar and the terms of

trade for the U.S. In response to a shock to China’s M2 the increase in the price deflator for

U.S. imports is significantly greater than the increase in the price deflator for U.S. exports.

The increase in import prices due to decline in the trade-weighted U.S. dollar and the increase

in oil (and commodity) prices account for the association of innovations to China’s M2 with

increases in U.S. consumer price inflation. These findings are robust to a number of model

specifications including different assumptions about whether commodity or oil prices appear

17

in the model, when U.S. variables to respond contemporaneously to Chinese M2, when

China’s M2 may respond to all U.S. domestic variables, and when the real trade-weighted

value of the U.S. dollar responds contemporaneously to China’s M2.

The inclusion of China variables in analysis of the international transmission of

monetary shocks is an appropriate specification given the tremendous impact of China on the

global economy in recent years. It is now clear that China’s economy has achieved a scale

such that China’s monetary policy and exchange rate policy have influence on the U.S.

economy. China’s growth influences world commodity/oil prices and this is reflected in

significantly higher inflation in the U.S. China’s monetary expansion is also associated with

significant decreases in the real trade weighted value of the U.S. dollar that is due to the

operation of a pegged currency. China pegs its currency to a basket of currencies in which the

U.S. dollar has considerable weight and also has extensive capital controls in place. To

maintain the value of its currency at a time of monetary expansion, China in effect takes steps

that result in a devaluation of real trade-weighted value of the U.S. dollar. In terms of the

Mundell–Fleming model, with imperfect capital mobility, sterilization actions under a fixed

exchange rate permit China to pursue an independent monetary policy with consequences for

the U.S.

References

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commodity prices. Bank of Italy Working Paper, Number 851.

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evaluation. European Economic Review 42(6), 1069-1112.

Bernanke, B., 1986. Alternative explanations of the money-income correlation. Carnegie-

Rochester series on Public Policy 25, 49-99.

Cai, Z., Chen, L. and Fang, Y., 2012, \A new forecasting model for USD/CNY exchange

rate", Studies in Nonlinear Dynamics & Econometrics, 16(2), forthcoming.

Canova, F., 2005. The transmission of US shocks to Latin America. Journal of Applied

Econometrics 20, 229–251.

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Christiano, L.J., Eichenbaum, M., Evans, C., 1999. Monetary policy shocks: What have we

learned and to what end? In: Taylor, J.B., Woodford, M. (Eds.), Handbook of

Macroeconomics, Vol. 1A. North-Holland, Amsterdam, 65–148.

Dedola, L., Lippi, F., 2005. The monetary transmission mechanism: Evidence from the

industries of five OECD countries. European Economic Review 49(6), 1543-1569.

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economies: The rising weights. Bank of Finland BOFIT Discussion Papers, 2-2012.

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CESifo Economic Studies 53 (1), 2-41.

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Equity Markets. The World Economy 35(7), 816-837.

Kilian, L., and B. Hicks (2012), “Did Unexpectedly Strong Economic Growth Cause the Oil

Price Shock of 2003-2008?” Journal of Forecasting, forthcoming.

Kim, S., Roubini, N., 2000. Exchange rate anomalies in the industrial countries: a solution

with a structural VAR approach. Journal of Monetary Economics 45, 561–586.

Kim, S., 2001a. International transmission of US Monetary policy shocks: evidence

from VARs. Journal of Monetary Economics 48, 339–372.

Kim, S., 2001b. Effects of monetary policy shocks on the trade balance in small open

European countries, Economics Letters 71(2), 197-203.

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South-East Asia. Economics of Transition 17 (1), 121–45.

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103, 624–660.

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Ratti, R.A., Vespignani, J.L., 2013. Liquidity and Crude Oil Prices: China’s Influence Over

1997-2011. Economic Modelling 33, 517-525.

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Sims, C.A., 1980. Macroeconomics and Reality. Econometrica 48, 1-48.

Sims, C.A., Zha, T., 1995. Does monetary policy generate recessions?: Using less aggregate

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20

Table 1: Test for unit roots 1996:1-2012:12:

Endogenous variables

Variables ADF KPSS PP ADF KPSS

0.999 1.793*** 0.0399*** 0.312

0.733 1.022*** 0.000*** 0.068

0.975 1.796*** 0.000*** 0.077

0.892 1.805*** 0.000*** 0.062

0.119 0.946*** 0.011*** 0.234

0.650 1.639*** 0.000*** 0.036

0.786 1.343*** 0.000*** 0.194 Notes: The variables are China M2, short term U.S. interest rate (U.S. IR), U.S. M2, U.S. consumer price index (U.S. CPI),

U.S. industrial production (U.S. IP), global oil price (OP), and real effective trade-weighted U.S. foreign exchange rate (U.S.

TWI). The null hypothesis for the Augmented Dickey-Fuller (ADF) test is the variable has a unit root and the null hypothesis

for the Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test is the variable is stationary. The first difference of the series is

indicated by ∆.The lag selection criteria for the ADF is based on Schwarz information Criteria (SIC) and for the KPSS is the

Newey-West Bandwidth. ***, **, * indicates rejection of the null hypothesis at 1%, 5% and 10%, levels of significance.

Table 2: Log likelihood ratio test for over-identification restrictions

Null Hypothesis : Restrictions are valid

Model restrictions 3 4 5 6 7

Chi-square value 10.5 8.16 4.49 6.89 9.01

Notes: Log likelihood ratio for over-identification test Chi-square values are reported for each of the five models

shown in equations (3), (4), (5), (6) and (7). The test is for non-recursive identification restrictions in the

contemporaneous matrix restrictions in equations (3), (4), (5), (6) and (7), based on equations (1) and (2). The

highest value for over-identification test restriction is for model of choice in equation (3), indicating that the

restriction cannot be rejected at higher significant level than for the other models.

21

Figure 1: Monetary aggregate M2 in billions of U.S. dollars for China and the U.S.

Notes: U.S. M2 data are from the Federal Reserve of St. Louis (FRED) and China M2 data are from People’s

Bank of China.

Figure 2: China/U.S. exchange rate and real effective trade-weighted U.S. dollar

Notes: China/U.S. exchange rate and real effective trade-weighted U.S. dollar (U.S. TWI) from the Federal

Reserve of St. Louis (FRED).

0

3000

6000

9000

12000

15000

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

U.S. M2 in Billions of USD

Chinese M2 in billions of USD

6

7

8

9

60

70

80

90

100

110

120

U.S. TWI (LHS)

Chinese yuan to one USD (RHS)

22

Figure 3: Response of U.S. economy to China M2 shocks: 1996:1-2012:12

Notes: Figure 3 shows the dynamic response or impulse response function of the U.S. variables in the SVAR

model equation (3) to positive China M2 shocks. The confidence bands are obtained using Monte Carlo

integration as described by Sims (1980), where 5000 draws were used from the asymptotic distribution of the

VAR coefficient. The variables are China M2, short term U.S. interest rate (USIR), U.S. M2 (USM2), U.S.

consumer price index (US CPI), U.S. industrial production (US IP), global oil price (OP), and real effective

trade-weighted U.S. dollar exchange rate (US TWI).

Figure 4: Response of U.S. trade prices to China M2 shocks: 1996:1-2012:12

Notes: Figure 4 shows the dynamic response or impulse response function of the U.S. trade price variables from

an SVAR model based on equation (3) to positive China M2 shocks. The variables in Figure 4 are China M2,

the price deflator for U.S. exports, price deflator for U.S. imports, and U.S. terms of trade variable (U.S. export

price/U.S. import price).

23

Figure 5: Response of U.S. trade variables to China M2 shocks: 1996:1-2012:12

Notes: Figure 5 shows the dynamic response or impulse response function of the U.S. trade variables from a

SVAR model based on equation (3) to positive China M2 shocks. The variables in Figure 5 are China M2,

nominal U.S. exports, nominal U.S. imports, nominal U.S. trade balance, real U.S. exports, real U.S. imports,

and real U.S. trade balance.

Figure 6: Response of bilateral U.S.-China and U.S.-non-China bilateral trade variables

Notes: Figure 6 shows the dynamic response or impulse response function of the U.S. variables from a SVAR

model based on equation (3) to positive China M2 shocks. The variables in Figure 6 are China’s M2, and

variables given by bilateral exports, imports, and net nominal trade balance between either the U.S. and China or

the U.S. and the world exclusive of China.

24

Figure 7: Historical decomposition on Chinese monetary shocks on U.S. economy: 1997:1 to

2012:12

Notes: Figure 7 shows the cumulative contribution of structural shocks to China’s M2 to U.S. economic

variables. The variables are short term U.S. interest rate (USIR), U.S. M2 (USM2), U.S. consumer price index

(US CPI), U.S. industrial production (US IP), global oil price (OP), real effective trade-weighted U.S. dollar

exchange rate (US TWI), the real trade balance (RTB) and the terms of trade (TOT). The cumulative

contributions of structural shocks to the U.S. variables reported in Figure 7 are annual averages of the monthly

data (the moving average of the last 12 months) to improve the readability of the plot. The data are not

annualized.

-0.04%

-0.02%

0.00%

0.02%

0.04%

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Historical contribution of China M2 shocks on US CPI

-1.50%

-0.50%

0.50%

1.50%

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Historical contribution of China M2 shocks on US oil

-0.10%

-0.05%

0.00%

0.05%

0.10%

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Historical contribution of China M2 shocks on US TWI

-0.20%

-0.10%

0.00%

0.10%

0.20%

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Historical contribution of China M2 shocks on US TOT

-0.40%

-0.20%

0.00%

0.20%

0.40%

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Historical contribution of China M2 shocks on US RTB

-0.20%

-0.10%

0.00%

0.10%

0.20%

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Historical contribution of China M2 shocks on US IR

School of Economics and Finance Discussion Papers

2013-05 On Bootstrap Validity for Specification Tests with Weak Instruments, Firmin Doko Tchatoka 

2013-04 Chinese Monetary Expansion and the US Economy, Joaquin L Vespignani and Ronald A Ratti 

2013-03 International Monetary Transmission to the Euro Area: Evidence from the US, Japan and China,

Joaquin L Vespignani and Ronald A Ratti

2013-02 The impact of jumps and thin trading on realized hedge ratios? Mardi Dungey, Olan T. Henry, Lyudmyla Hvozdyk

2013-01 Why crude oil prices are high when global activity is weak?, Ronald A Rattia and Joaquin L Vespignani

2012-11 Crude Oil Prices and Liquidity, the BRIC and G3 Countries, Ronald A Ratti and Joaquin L Vespignani

2012-10 Crude Oil Prices: China’s Influence Over 1996-2011, Ronald A Ratti and Joaquin L Vespignani

2012-09 Exchange Rate Risk Exposure and the Value of European Firms, Fabio Parlapiano and Vitali Alexeev

2012-08 Ranking Systemically Important Financial Institutions, Mardi Dungey, Matteo Luciani and David Veredas

2012-07 Identification-Robust Inference for Endogeneity Parameters in Linear Structural Models, Firmin Doko Tchatoka and Jean-Marie Dufour

2012-06 Specification Tests with Weak and Invalid Instruments, Firmin Doko Tchatoka

2012-05 Liquidity and Crude Oil Prices: China’s Influence Over 1996-2011, Ronald A. Rattia and Joaquin L. Vespignani

2012-04 On the Validity of Durbin-Wu-Hausman Tests for Assessing Partial Exogeneity Hypotheses with Possibly Weak Instruments, Firmin Doko Tchatoka

2012-03 Endogenous Crisis Dating and Contagion Using Smooth Transition Structural GARCH, Mardi Dungey, George Milunovich, Susan Thorp and Minxian Yang

2012-02 Testing for Partial Exogeneity with Weak Identification, Firmin Doko Tchatoka

2012-01 On the Correspondence Between Data Revision and Trend-Cycle Decomposition, Mardi Dungey, Jan PAM Jacobs and Jian Tian

2011-06 Systematic and Liquidity Risk in Subprime-Mortgage Backed Securities, Mardi Dungey, Gerald P. Dwyer and Thomas Flavin

2011-05 A SVECM Model of the UK Economy and The Term Premium, Mardi Dungey and M. Tugrul Vehbi

2011-04 Do Contact Matter in the Process of Getting a Job in Cameroon? Firmin Doko Tchatoka and Urbain Thierry Yogo

2011-03 Subset Hypotheses Testing and Instrument Exclusion in the Linear IV Regression, Firmin Doko Tchatoka

2011-02 First home Buyers’ Support Schemes in Australia – Results Spreadsheet, Mardi Dungey, Graeme Wells and Sam Thompson

2011-01 First home Buyers’ Support Schemes in Australia, Mardi Dungey, Graeme Wells and Sam Thompson

Copies of the above mentioned papers and a list of previous years’ papers are available from our home site at http://www.utas.edu.au/economics‐finance/research/